An Inexpensive Deployable Acoustic Snow Observing Sensor (IDASOS) Sean Helfrich NESDIS Snow and Ice Product Area Lead National.

Slides:



Advertisements
Similar presentations
Mobile Wireless Sensor Network (mWSN) at Nokia
Advertisements

The IRIS-Bus Concept for Seismic Instrumentation John D. West Arizona State University.
SNOW SURVEY, SNOTEL (SNOwpack TELemetry) & SCAN (Soil Climate Analysis Network) Presented at NWS Cold Regions Workshop November , 2004.
Soil Moisture and Soil Temperature Observations and Applications: A Joint U.S. Climate Reference Network (USCRN) – National Integrated Drought Information.
Sensing Winter Soil Respiration Dynamics in Near-Real Time Alexandra Contosta 1, Elizabeth Burakowski 1,2, Ruth Varner 1, and Serita Frey 3 1 University.
Integrated sensing and modeling on a sensor node Yeonjeong Park and Tom Harmon UC Merced Environmental Systems program.
RFID Sensors Faculty Advisor: Dimitrios Peroulis.
Report from the GCOS Archive/Analysis Center Matthew Menne NOAA/National Centers for Environmental Information Center for Weather and Climate (NCEI-Asheville)
Sensor Networks: Next Generation Problems Frank Vernon Scripps Institution of Oceanography University of California at San Diego SAMSI Sensor Network Workshop.
Team Advisor: Professor Marinos Vouvakis Douglas Imbier EE Team Leader wireless, sensors Edmons Zongo EE Treasurer display, sensors Nicholas Ferrero EE.
Bad News Bots E2 Project Design Review 6 - 2/21/2008 Ryan Bove Kara Collins Peter Courtney Kyle O’Reilly Benjamin Rowland.
The Logistic Requirements for Wave Field Measurement Prof. Dr.-Ing. Chia Chuen Kao Director, Coastal Ocean Monitoring Center, National Cheng Kung University.
Mapping future snow cover in Idaho Brandon C. Moore University of Idaho.
ADAPTIVE TRAFFIC CONTROLLER Professor Doshi Peter Petrakis (team manager) Marcin Celmer Matt Wilhelm Tom Stack.
Wireless Ad Hoc Networks Mario Gerla CS 215, Winter 2001 Introduction to Ad Hoc networks Protocol Stack Physical and MAC Layer Clustering.
Idaho Climate and Water Resource Forecasts for the 2005 Water Year October 26, 2004 Sponsored by: The Climate Impacts Group (CIG) at the University of.
Chapter 5. Operations on Multiple R. V.'s 1 Chapter 5. Operations on Multiple Random Variables 0. Introduction 1. Expected Value of a Function of Random.
Problem Description: To develop an autonomous network for monitoring aquatic environment Problem Description: To develop an autonomous network for monitoring.
Surface Temperature Described scientifically, surface temperature is the radiating temperature of the ground surface including grass, bare soil, roads,
POLITECNICO DI TORINO TRIBUTE and DIMMER. DIMMER - The context One of the major challenges in today’s economy concerns the reduction in energy usage and.
The Remote Sensing of Winds Student: Paul Behrens Placement and monitoring of wind turbines Supervisor: Stuart Bradley.
Why We Care or Why We Go to Sea.
MICA: A Wireless Platform for Deeply Embedded Networks
Tower SystemsJanuary AMS Short Course on Instrumentation 1 Installation and Use of Meteorological Tower Systems Melanie A. Wetzel Desert Research.
Slide 1 Soil Moisture and Soil Temperature Observations and Applications Operated by the Natural Resources Conservation Service Garry L. Schaefer, WCM.
Modern Software Engineering for Distributed Embedded Systems Joseph Voelmle, Carlos Daboin, Joanne Sirois, Josh Gallegos Mentor: Dr. Janusz Zalewski.
Embedded sensor network design for spatial snowcover Robert Rice 1, Noah Molotch 2, Roger C. Bales 1 1 Sierra Nevada Research Institute, University of.
Spatially Complete Global Surface Albedos Derived from MODIS Data
High Value Research Sweet Sixteen Maryland State Highway Administration Allison Hardt.
Mark Heggli Innovative Hydrology, Inc. Consultant to the World Bank Expert Real-time Hydrological Technology Module 1: Essential Elements of a Hydrological.
Design and Application Spaces for 6LoWPAN (draft-ekim-6lowpan-scenarios-02) IETF-71 Philadelphia Tuesday, March Eunsook Kim, Nicolas Chevrollier,
Earth Observation from Satellites GEOF 334 MICROWAVE REMOTE SENSING A brief introduction.
Applying New Drought Decision Support Tools Mark Svoboda National Drought Mitigation Center International Drought Information Center University of Nebraska-Lincoln.
Fair Sharing of MAC under TCP in Wireless Ad Hoc Networks Mario Gerla Computer Science Department University of California, Los Angeles Los Angeles, CA.
Water Cycle Breakout Session Attendees: June Wang, Julie Haggerty, Tammy Weckwerth, Steve Nesbitt, Carlos Welsh, Vivek, Kathy Sharpe, Brad Small Two objectives:
Instrumented Wheel For Wheelchair Propulsion Assessment. Jacob Connelly Andrew Cramer John Labiak.
Images and Sounds: Audio and Video for Education Joe Wise and Michael Hamilton.
Development and evaluation of Passive Microwave SWE retrieval equations for mountainous area Naoki Mizukami.
OOI Annual Review Year 2 May 16 – 20, 2011 Ocean Observatories Initiative Surface and Subsurface Mooring Telemetry Inductive and acoustic technology and.
Michael A. Palecki USCRN Science Project Manager National Climatic Data Center DOC/NOAA/NESDIS USCRN PROGRAM STATUS MARCH 3, United States Climate.
Computer Engineering and Networks Technische Informatik und Kommunikationsnetze PermaSense Sensing in Disruptive Environments Jan Beutel.
Lecture 22 Deployment Strategies Fixed Platforms Collin Roesler 18 July 2007.
Why We Care or Why We Go to Sea.
Future PermaSense Challenges – Technology Jan Beutel.
A Prototype Network for Measuring Arctic Winter Precipitation and Snow Cover (Snow-Net) Matthew Sturm, CRREL Doug Kane, UAF Svetlana Berezovskaya, UAF.
Libby Dam June 2006 Spill: Total Dissolved Gas Monitoring.
Wireless Sensor Network (WSN). WSN - Basic Concept WSN is a wireless network consisting of spatially distributed autonomous devices using sensors to cooperatively.
July 31, 2012 Kevin Werner NWS Colorado Basin River Forecast Center Tim Bardsley Western Water Assessment 1 Future Colorado Basin Observing System.
Polar Mapping Update and WMS Experience Shinobu Kawahito Remote Sensing Technology Center of Japan (RESTEC) in support of Japan Aerospace Exploration Agency.
Deployment of Wisden: In a real environment - Four Seasons Building Deployment of Wisden: In a real environment - Four Seasons Building Results from Deployment:
Goal: to understand carbon dynamics in montane forest regions by developing new methods for estimating carbon exchange at local to regional scales. Activities:
A. Hangan, L. Vacariu, O. Cret, H. Hedesiu Technical University of Cluj-Napoca A Prototype for the Remote Monitoring of Water Parameters.
Background Real-time environmental monitoring is a field garnering an ever-increasing amount of attention. The ability for sensors to make and publish.
A Prototype for the Continuous and Cost-Effective Measurement of River Discharge.
Mobile Node for Wireless Sensor Network to Detect Landmines Presented by : Jameela Hassan.
Revisimo Remote Vital Sign Monitor. About us Dr. Thomas Morris Advisor Cory Zywno EE Data Transmission Matthew Galloway EE ADC & Temperature Sensor Matthew.
Wireless Charging of Mobile Phones Using Microwaves
Pritee Parwekar. Requirements and Standards Some requirements for WSN deployment include: –Fault tolerance –Lifetime –Scalability –Real-time data.
An Inexpensive, Rapid Response System for Wind Profile Assessment, particularly for Shear Layer Determination and Wind Turbine Location Tom Wilkerson,
배관의 내부검사용 이동로봇 및 조향장치 등록번호 : , 특허권자 : 최 혁 렬 외 2 명.
Slide 1 U. S. Drought Monitor Forum Automated Soil Moisture Monitoring From CSCAN and SNOTEL Networks Garry L. Schaefer, WCM Branch Leader October 10,
Big Data at Low Cost.
Team #3: Group Members Adam Davis Tony Johnson Peter Meyer Isaac Krull
Upper Rio Grande studies around 6 snow telemetry (SNOTEL) sites
Instrumentation & Measurement (ME342)
Measuring mountain water cycle at the basin scale
Kostas M. Andreadis1, Dennis P. Lettenmaier1
Stochastic Storm Rainfall Simulation
Differences between WSN & MANET By: Dana Alotaibi.
Presentation transcript:

An Inexpensive Deployable Acoustic Snow Observing Sensor (IDASOS) Sean Helfrich NESDIS Snow and Ice Product Area Lead National Ice Center

Introduction What is it: What is it: A low cost instrument designed to over come the requirement of multiple snow observations over an area. What purpose does it serve: What purpose does it serve: Snow depth is spatially heterogeneous and proper assessment requires a high spatial sampling rate over a broad area to account for the variability, requiring resources and time from observers. Typical automated snow depth measurements can be cost prohibitive. Cost of instruments often forces observers to limit the number of instruments taking samples. IDASOS hopes to overcome this limitation in automation.

Acoustic Range Finder Datalogger / Microprocessor Temperature Diode Battery Pack 18 inch stem from base IDASOS Prototype Parts About $200 for this prototype

IDASOS Site Locations 2010

Joe Wright SnoTel vs IDASOS

CSU site vs IDASOS

What is next? Test for wireless transmission (Bluetooth?) Test for wireless transmission (Bluetooth?) Increase Power battery pack lasted 5 months in at 10k feet Increase Power battery pack lasted 5 months in at 10k feet Real-time data processing code Real-time data processing code Increase samples to 20,000 for datalogger. Increase samples to 20,000 for datalogger. Configure in an array to access snow variability for remote sensing calibration Configure in an array to access snow variability for remote sensing calibration Other utilities? Other utilities?

ANY QUESTIONS?